Innovation and Disruption

Robotic Disruption and the New Healthcare Revenue Cycle

August 23, 2017 4:56 pm

Robotic process automation represents an immediate new opportunity for healthcare organizations to perform repetitive, ongoing revenue cycle processes more efficiently and accurately.

The nation’s healthcare system is on the brink of a profound transformation with the emergence of new robotic applications that automate processes and help realize the quality improvements, savings, and cost reductions required for success under value-based care. With the relatively recent advent of robotic process automation (RPA) in health care, healthcare organizations are capturing the same cost savings and quality improvements that manufacturers have achieved in the past 40 years.

RPA is a software-based protocol that operates across technology platforms at the user interface level to mimic human actions. This robotic software can replicate many routine tasks currently performed by revenue cycle staff using existing software applications, websites, and business productivity tools. Although there are myriad possible applications for RPA in health care, as shown in the exhibit below, a good starting point is the revenue cycle due to its relative share of total operating expenses, high level of manual processing, and impact on revenue performance.

Opportunities Presented by Robotic Process Automation

Potential Benefits and Uses of RPA

Cost reduction opportunities are one self-evident, and enticing, opportunity, but software robots also can affect provider organizations through other avenues—from quality to patient experience, and from data accuracy to net revenue improvement. Tactically, organizations can enhance their financial performance through RPA. Strategically, new ways of doing business can be enabled.

RPA has the potential for application in many areas of the provider landscape. Processes that are good candidates for RPA exhibit one or more of the following characteristics:

  • Rules-based and repetitive
  • Prone to human error
  • Mid- to high-volume
  • Seasonal with unpredictable peaks and troughs
  • Need for out-of-office support
  • Nonpriority for the IT department
  • Lack of integrated applications

Although identifying those high-volume, largely rules-based processes can be a straightforward exercise, understanding how an organization can leverage RPA enterprisewide and create a platform for transformation can be more challenging.

The past decade has seen a renewed focus on data analytics and data monitoring, unveiling a new level of detail and transparency. Human workers are capable of logging errors and monitoring common trends they are identifying, but even the most diligent of workers will have difficulty identifying 100 percent of his or her actions, the exceptions those actions create, and how they are—or are not—successfully handled. RPA enables transactions to be monitored with increased effectiveness that is augmented by a much higher and more detailed level of data collection. The associated large data sets help inform decisions and identify trends, ultimately enabling organizations to anticipate change.

RPA Entry Point: The Revenue Cycle

Initial RPA deployments will likely focus on simple rules-based logic, such as is inherent in many revenue-cycle-related tasks. The following are a few revenue cycle areas in which RPA holds immediate promise for healthcare organizations.

Financial clearance. Using robots to understand the necessary financial-clearance-related tasks for a given patient, and then access a variety of websites or applications to complete them, is a game-changer for health systems. This activity is one of the most error-prone areas of the revenue cycle and often leads to significant downstream denials and delays in payment. Precise financial-clearance processing can result in not only incremental net revenue but also reduced downstream account touches by revenue cycle staff.

Denials management. Basic denials such as coordination of benefits and eligibility on date of service are prime candidates for automation. Managing these denials involves a specific set of tasks that a user would complete before resubmitting the claim to the health plan. Resolving coordination-of-benefits denials typically involves identifying the alternative insurance coverage in the patient accounting system, revising the claim, then submitting the claim to the alternative payer. Although these denials may be simple, many health systems tend to experience a high volume of such errors. To correct such denials, robotic programming can be prepared in accordance with the rules associated with the denial type and insurance combination, to replicate the human resolution process.

Credit balances. If programmed into a fixed script, the same figure from the same health plan can be identified and the system can be set up to perform the same activity in transaction posting each time the value is encountered. The reversal of the credit balance through automated transaction posting can eliminate hundreds, if not thousands, of transactions over time, resulting in cost savings and the opportunity to reallocate staff to higher-value activities.

Change not only is inevitable, but also can quickly leave a company behind. In an August 2016 HealthCatalyst survey of hospital executives, 80 percent of responding provider leaders said they believe predictive capabilities will be important for the future of health care—but only 31 percent of hospitals have had the required capabilities in place for more than a year. a We are likely to witness the continued evolution of automation and cognitive capabilities in coming years. The best advice for providers that want to maintain a competitive advantage is to choose to lead the charge rather than be spectators in the automation revolution.

CHI: Moving Toward Automation

One organization that is pioneering the use of RPA in health care is Catholic Health Initiatives (CHI) in Denver. CHI’s leaders recognized that the health system’s future financial and operational success depended on moving quickly to develop RPA capabilities. CHI is the nation’s third-largest not-for-profit health system, consisting of 102 hospitals located in 17 states plus numerous other facilities and services spanning the inpatient and outpatient continuum of care. The health system generated operating revenues of $15.9 billion in 2016, so efficiency in all aspects of CHI’s operational management is critical.

CHI initiated its select use of RPA within accounts payable (A/P) beginning in early 2017, where it is projected to achieve more than $0.5 million in annual recurring savings through automated A/P processing. With that foundation in place, CHI plans to aggressively expand its RPA capabilities throughout the organization to include additional financial transaction and payer compliance processes defined by manual and rote tasks.

“The lessons learned from our initial RPA successes will enable us to dramatically increase organizational productivity, reduce regulatory risk, and improve margins,” says Danielle Weber, CHI’s senior vice president of finance and revenue.

Sidebar: An Overview of RPA

Future-State Capabilities

As organizations become more comfortable with their robotic capabilities, advanced features such as handwriting recognition, natural language processing, pattern-detection through machine learning, and cognitive processing also drive more sophisticated automations. Establishing a core competency in RPA enables organizations to begin to incorporate advanced cognitive capabilities, shown in the exhibit below, that can drive incremental value.

Opportunities to Move Beyond Robotic Process Automation

It is important to recognize RPA as a first step in a trajectory of continued technological growth. Although the topics initially discussed highlight some of the key indicators for RPA process candidacy, such as high-volume and largely rules-based processes, these considerations merely set the stage for continued evolution, including growth into the spaces of cognitive capabilities and, eventually, artificial intelligence. The application of such technologies in health care may be here sooner than previously assumed.

The Business 4.0 Revolution and Cognitive Computing

Machine learning is a field of computer science that enables computers to identify patterns, like-events, and activities that have similar characteristics. Machine-learning algorithms are designed to generate predictive outputs based on the processing of training data sets. As those data sets continue to grow—whether from connected devices, biosensors, digital health, social media, or other sources—data scientists will likely be able to harness that information to drive the evolution and efficacy of these algorithms. For healthcare finance, machine learning has tremendous applicability to bring greater technological sophistication to automated processes in the revenue cycle, such as payment variance identification and remediation for complex payment methodologies.

Machine learning can be used for improved management of variances in payment transactions, for example.To this end, it is possible to apply advanced cognitive capabilities to identify the similarities in payment transactions and allow for more programmatic transaction posting. An analyst can monitor these transactions for accuracy, and the outliers can be examined and incorporated into the cognitive platform for a more informed automated process.

How to Get Started

Although establishing a core competency as transformative as RPA can sound intimidating, simple tools already exist to make this a much more tangible reality. With a specific focus on revenue cycle processes, pilots to prove the value of robotic automation within an organization can be implemented within weeks.

The break-even time for RPA is in months, not years, due to the small up-front investment and short implementation time. Moreover, an RPA implementation has the advantage of being a noninvasive process, which means such a project can be established without significant disruptions to core operations or huge capital outlays. Given the limited financial and resource investments required to experiment with RPA, providers are overcoming initial anxieties and exploring its application to healthcare operations.

Jerry Bruno 
is a principal with Deloitte Consulting, Stamford, Conn., and a member of HFMA’s Connecticut Chapter.

Sam Johnson, MBA, MHA, 
is a senior manager with Deloitte Consulting, Atlanta, and a member of HFMA’s Georgia Chapter.

Josh Hesley, MHA, 
is a manager with Deloitte Consulting, Atlanta.


a. HealthCatalyst Hospital Executive Survey, August 2016.


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